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Watershed‐scale spatial prediction of agricultural land phosphorus mass balance and soil phosphorus metrics: A bottom‐up approach
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2024
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Source: Journal of Environmental Quality (2024)
[PDF-2.64 MB]
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Journal Title:Journal of Environmental Quality
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Description:Analysis of nutrient balance at the watershed scale, including for phosphorus (P), is typically accomplished using aggregate input datasets, resulting in an inability to capture the variability of P status across the study region. This study presents a set of methods to predict and visualize partial P mass balance, soil P saturation ratio (PSR), and soil test P for agricultural parcels across a watershed in the Lake Champlain Basin (Vermont, USA) using granular, field‐level data. K‐means cluster analyses were used to group agricultural parcels by soil texture, average slope, and crop type. Using a set of parcels accounting for ∼21% of the watershed's agricultural land and having known soil test and nutrient management parameters, predictions of partial P mass balance, PSR, and soil test P for agricultural land across the watershed were made by cluster, incorporating uncertainty. This resulted in an average partial P balance of 5.5 ± 0.2 kg P ha−1 year−1 and an average PSR of 0.0399 ± 0.0002. Furthermore, approximately 30% of agricultural land had predicted soil test P values above optimum levels. Results were used to visualize areas with high P loss potential. Such data and visualizations can inform watershed P modeling and assist practitioners in nutrient management decision making. These techniques can also serve as a framework for bottom‐up modeling of nutrient mass balance and soil metrics in other regions.
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Source:Journal of Environmental Quality (2024)
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DOI:
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ISSN:0047-2425;1537-2537;
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Rights Information:CC BY-NC-ND
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Compliance:Submitted
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